Članki z zahtevami za javni dostop - Wolfgang MaassVeč o tem
Ni na voljo nikjer: 2
Brain computation: a computer science perspective
W Maass, CH Papadimitriou, S Vempala, R Legenstein
Computing and Software Science: State of the Art and Perspectives, 184-199, 2019
Zahteve: US National Science Foundation
Recursively Invariant-Recursion Theory
W Maass
Recursion Theory and Computational Complexity, 227-240, 2011
Zahteve: German Research Foundation
Na voljo nekje: 54
State-dependent computations: spatiotemporal processing in cortical networks
DV Buonomano, W Maass
Nature Reviews Neuroscience 10 (2), 113-125, 2009
Zahteve: US National Institutes of Health, Austrian Science Fund
Long short-term memory and learning-to-learn in networks of spiking neurons
G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass
Advances in neural information processing systems 31, 2018
Zahteve: European Commission
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Neuromorphic Computing and Engineering 2 (2), 022501, 2022
Zahteve: US National Science Foundation, Swiss National Science Foundation, US …
Towards a theoretical foundation for morphological computation with compliant bodies
H Hauser, AJ Ijspeert, RM Füchslin, R Pfeifer, W Maass
Biological cybernetics 105, 355-370, 2011
Zahteve: Austrian Science Fund
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
R Legenstein, D Pecevski, W Maass
PLoS computational biology 4 (10), e1000180, 2008
Zahteve: Austrian Science Fund
Liquid state machines: motivation, theory, and applications
W Maass
Computability in context: computation and logic in the real world, 275-296, 2011
Zahteve: Austrian Science Fund
Inferring spike trains from local field potentials
MJ Rasch, A Gretton, Y Murayama, W Maass, NK Logothetis
Journal of neurophysiology 99 (3), 1461-1476, 2008
Zahteve: Austrian Science Fund
A learning rule for very simple universal approximators consisting of a single layer of perceptrons
P Auer, H Burgsteiner, W Maass
Neural networks 21 (5), 786-795, 2008
Zahteve: Austrian Science Fund
Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system
S Schmitt, J Klähn, G Bellec, A Grübl, M Guettler, A Hartel, S Hartmann, ...
2017 international joint conference on neural networks (IJCNN), 2227-2234, 2017
Zahteve: European Commission
Distributed fading memory for stimulus properties in the primary visual cortex
D Nikolić, S Häusler, W Singer, W Maass
PLoS biology 7 (12), e1000260, 2009
Zahteve: Austrian Science Fund, German Research Foundation
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task
R Legenstein, SM Chase, AB Schwartz, W Maass
Journal of Neuroscience 30 (25), 8400-8410, 2010
Zahteve: US National Institutes of Health, Austrian Science Fund
STDP enables spiking neurons to detect hidden causes of their inputs
B Nessler, M Pfeiffer, W Maass
Advances in neural information processing systems 22, 2009
Zahteve: Austrian Science Fund
Network plasticity as Bayesian inference
D Kappel, S Habenschuss, R Legenstein, W Maass
PLoS computational biology 11 (11), e1004485, 2015
Zahteve: Austrian Science Fund
The role of feedback in morphological computation with compliant bodies
H Hauser, AJ Ijspeert, RM Füchslin, R Pfeifer, W Maass
Biological cybernetics 106, 595-613, 2012
Zahteve: Austrian Science Fund
Brain computation by assemblies of neurons
CH Papadimitriou, SS Vempala, D Mitropolsky, M Collins, W Maass
Proceedings of the National Academy of Sciences 117 (25), 14464-14472, 2020
Zahteve: US National Science Foundation
Spike frequency adaptation supports network computations on temporally dispersed information
D Salaj, A Subramoney, C Kraisnikovic, G Bellec, R Legenstein, W Maass
Elife 10, e65459, 2021
Zahteve: Austrian Science Fund, European Commission
Memory-efficient deep learning on a SpiNNaker 2 prototype
C Liu, G Bellec, B Vogginger, D Kappel, J Partzsch, F Neumärker, ...
Frontiers in neuroscience 12, 840, 2018
Zahteve: UK Engineering and Physical Sciences Research Council, European Commission
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning
D Kappel, R Legenstein, S Habenschuss, M Hsieh, W Maass
eneuro 5 (2), 2018
Zahteve: European Commission
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